Overview

Dataset statistics

Number of variables12
Number of observations15000
Missing cells3676
Missing cells (%)2.0%
Duplicate rows30
Duplicate rows (%)0.2%
Total size in memory1.4 MiB
Average record size in memory96.0 B

Variable types

Numeric8
Categorical4

Alerts

Dataset has 30 (0.2%) duplicate rowsDuplicates
url has a high cardinality: 13438 distinct valuesHigh cardinality
make has a high cardinality: 63 distinct valuesHigh cardinality
model has a high cardinality: 524 distinct valuesHigh cardinality
manufactured is highly overall correlated with no_of_owners and 1 other fieldsHigh correlation
power is highly overall correlated with engine_cap and 2 other fieldsHigh correlation
engine_cap is highly overall correlated with power and 2 other fieldsHigh correlation
curb_weight is highly overall correlated with power and 2 other fieldsHigh correlation
no_of_owners is highly overall correlated with manufactured and 1 other fieldsHigh correlation
mileage is highly overall correlated with manufactured and 2 other fieldsHigh correlation
price is highly overall correlated with power and 2 other fieldsHigh correlation
make is highly overall correlated with engine_cap and 1 other fieldsHigh correlation
type_of_vehicle is highly overall correlated with makeHigh correlation
url has 1503 (10.0%) missing valuesMissing
make has 987 (6.6%) missing valuesMissing
mileage has 741 (4.9%) missing valuesMissing
price has 445 (3.0%) missing valuesMissing
url is uniformly distributedUniform

Reproduction

Analysis started2023-09-02 16:09:20.341548
Analysis finished2023-09-02 16:09:49.233086
Duration28.89 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

listing_id
Real number (ℝ)

Distinct14925
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011520.4
Minimum540570
Maximum1031324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:49.538509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum540570
5-th percentile973173.85
Q11004436.5
median1018245.5
Q31025465.5
95-th percentile1030174.1
Maximum1031324
Range490754
Interquartile range (IQR)21029

Descriptive statistics

Standard deviation22228.693
Coefficient of variation (CV)0.021975527
Kurtosis36.663138
Mean1011520.4
Median Absolute Deviation (MAD)8885
Skewness-3.9997439
Sum1.5172805 × 1010
Variance4.9411481 × 108
MonotonicityNot monotonic
2023-09-03T00:09:49.899335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014871 3
 
< 0.1%
1013287 2
 
< 0.1%
1007571 2
 
< 0.1%
982101 2
 
< 0.1%
1025188 2
 
< 0.1%
1000777 2
 
< 0.1%
1002390 2
 
< 0.1%
979943 2
 
< 0.1%
1021331 2
 
< 0.1%
1027371 2
 
< 0.1%
Other values (14915) 14979
99.9%
ValueCountFrequency (%)
540570 1
< 0.1%
691782 1
< 0.1%
708011 1
< 0.1%
733385 1
< 0.1%
743986 1
< 0.1%
749258 1
< 0.1%
756142 1
< 0.1%
759456 1
< 0.1%
767227 1
< 0.1%
773433 1
< 0.1%
ValueCountFrequency (%)
1031324 1
< 0.1%
1031322 1
< 0.1%
1031319 1
< 0.1%
1031316 1
< 0.1%
1031314 1
< 0.1%
1031312 1
< 0.1%
1031310 1
< 0.1%
1031309 1
< 0.1%
1031306 1
< 0.1%
1031305 1
< 0.1%

url
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct13438
Distinct (%)99.6%
Missing1503
Missing (%)10.0%
Memory size117.3 KiB
https://www.sgcarmart.com/listing/1013488
 
2
https://www.sgcarmart.com/listing/979943
 
2
https://www.sgcarmart.com/listing/1029863
 
2
https://www.sgcarmart.com/listing/1009323
 
2
https://www.sgcarmart.com/listing/995501
 
2
Other values (13433)
13487 

Length

Max length41
Median length41
Mean length40.800548
Min length40

Characters and Unicode

Total characters550685
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13379 ?
Unique (%)99.1%

Sample

1st rowhttps://www.sgcarmart.com/listing/1023911
2nd rowhttps://www.sgcarmart.com/listing/1022346
3rd rowhttps://www.sgcarmart.com/listing/1017880
4th rowhttps://www.sgcarmart.com/listing/1022468
5th rowhttps://www.sgcarmart.com/listing/1026440

Common Values

ValueCountFrequency (%)
https://www.sgcarmart.com/listing/1013488 2
 
< 0.1%
https://www.sgcarmart.com/listing/979943 2
 
< 0.1%
https://www.sgcarmart.com/listing/1029863 2
 
< 0.1%
https://www.sgcarmart.com/listing/1009323 2
 
< 0.1%
https://www.sgcarmart.com/listing/995501 2
 
< 0.1%
https://www.sgcarmart.com/listing/894308 2
 
< 0.1%
https://www.sgcarmart.com/listing/992077 2
 
< 0.1%
https://www.sgcarmart.com/listing/1014871 2
 
< 0.1%
https://www.sgcarmart.com/listing/1021166 2
 
< 0.1%
https://www.sgcarmart.com/listing/1027455 2
 
< 0.1%
Other values (13428) 13477
89.8%
(Missing) 1503
 
10.0%

Length

2023-09-03T00:09:50.299176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.sgcarmart.com/listing/1013488 2
 
< 0.1%
https://www.sgcarmart.com/listing/1023758 2
 
< 0.1%
https://www.sgcarmart.com/listing/981956 2
 
< 0.1%
https://www.sgcarmart.com/listing/1020775 2
 
< 0.1%
https://www.sgcarmart.com/listing/995581 2
 
< 0.1%
https://www.sgcarmart.com/listing/1024788 2
 
< 0.1%
https://www.sgcarmart.com/listing/988230 2
 
< 0.1%
https://www.sgcarmart.com/listing/1021331 2
 
< 0.1%
https://www.sgcarmart.com/listing/1007674 2
 
< 0.1%
https://www.sgcarmart.com/listing/996726 2
 
< 0.1%
Other values (13428) 13477
99.9%

Most occurring characters

ValueCountFrequency (%)
/ 53988
 
9.8%
t 53988
 
9.8%
s 40491
 
7.4%
w 40491
 
7.4%
i 26994
 
4.9%
. 26994
 
4.9%
g 26994
 
4.9%
c 26994
 
4.9%
a 26994
 
4.9%
r 26994
 
4.9%
Other values (17) 199763
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 364419
66.2%
Other Punctuation 94479
 
17.2%
Decimal Number 91787
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 53988
14.8%
s 40491
11.1%
w 40491
11.1%
i 26994
7.4%
g 26994
7.4%
c 26994
7.4%
a 26994
7.4%
r 26994
7.4%
m 26994
7.4%
h 13497
 
3.7%
Other values (4) 53988
14.8%
Decimal Number
ValueCountFrequency (%)
1 19024
20.7%
0 18310
19.9%
2 10503
11.4%
9 9283
10.1%
8 6341
 
6.9%
3 6102
 
6.6%
7 5852
 
6.4%
6 5621
 
6.1%
5 5409
 
5.9%
4 5342
 
5.8%
Other Punctuation
ValueCountFrequency (%)
/ 53988
57.1%
. 26994
28.6%
: 13497
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 364419
66.2%
Common 186266
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 53988
14.8%
s 40491
11.1%
w 40491
11.1%
i 26994
7.4%
g 26994
7.4%
c 26994
7.4%
a 26994
7.4%
r 26994
7.4%
m 26994
7.4%
h 13497
 
3.7%
Other values (4) 53988
14.8%
Common
ValueCountFrequency (%)
/ 53988
29.0%
. 26994
14.5%
1 19024
 
10.2%
0 18310
 
9.8%
: 13497
 
7.2%
2 10503
 
5.6%
9 9283
 
5.0%
8 6341
 
3.4%
3 6102
 
3.3%
7 5852
 
3.1%
Other values (3) 16372
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 550685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 53988
 
9.8%
t 53988
 
9.8%
s 40491
 
7.4%
w 40491
 
7.4%
i 26994
 
4.9%
. 26994
 
4.9%
g 26994
 
4.9%
c 26994
 
4.9%
a 26994
 
4.9%
r 26994
 
4.9%
Other values (17) 199763
36.3%

make
Categorical

HIGH CARDINALITY  HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)0.4%
Missing987
Missing (%)6.6%
Memory size117.3 KiB
mercedes-benz
2055 
toyota
1868 
honda
1801 
bmw
1779 
audi
729 
Other values (58)
5781 

Length

Max length13
Median length11
Mean length6.6709484
Min length2

Characters and Unicode

Total characters93480
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st rowtoyota
2nd rowtoyota
3rd rowcitroen
4th rowrenault
5th rowmercedes-benz

Common Values

ValueCountFrequency (%)
mercedes-benz 2055
13.7%
toyota 1868
12.5%
honda 1801
12.0%
bmw 1779
11.9%
audi 729
 
4.9%
mazda 608
 
4.1%
volkswagen 575
 
3.8%
hyundai 512
 
3.4%
nissan 462
 
3.1%
kia 413
 
2.8%
Other values (53) 3211
21.4%
(Missing) 987
 
6.6%

Length

2023-09-03T00:09:50.614377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mercedes-benz 2055
14.5%
toyota 1868
13.2%
honda 1801
12.7%
bmw 1779
12.6%
audi 729
 
5.2%
mazda 608
 
4.3%
volkswagen 575
 
4.1%
hyundai 512
 
3.6%
nissan 462
 
3.3%
kia 413
 
2.9%
Other values (56) 3343
23.6%

Most occurring characters

ValueCountFrequency (%)
e 10621
 
11.4%
a 8900
 
9.5%
o 7679
 
8.2%
n 6699
 
7.2%
d 5955
 
6.4%
s 5829
 
6.2%
m 5238
 
5.6%
t 4719
 
5.0%
b 4656
 
5.0%
i 4251
 
4.5%
Other values (17) 28933
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91252
97.6%
Dash Punctuation 2096
 
2.2%
Space Separator 132
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10621
 
11.6%
a 8900
 
9.8%
o 7679
 
8.4%
n 6699
 
7.3%
d 5955
 
6.5%
s 5829
 
6.4%
m 5238
 
5.7%
t 4719
 
5.2%
b 4656
 
5.1%
i 4251
 
4.7%
Other values (15) 26705
29.3%
Dash Punctuation
ValueCountFrequency (%)
- 2096
100.0%
Space Separator
ValueCountFrequency (%)
132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91252
97.6%
Common 2228
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10621
 
11.6%
a 8900
 
9.8%
o 7679
 
8.4%
n 6699
 
7.3%
d 5955
 
6.5%
s 5829
 
6.4%
m 5238
 
5.7%
t 4719
 
5.2%
b 4656
 
5.1%
i 4251
 
4.7%
Other values (15) 26705
29.3%
Common
ValueCountFrequency (%)
- 2096
94.1%
132
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10621
 
11.4%
a 8900
 
9.5%
o 7679
 
8.2%
n 6699
 
7.2%
d 5955
 
6.4%
s 5829
 
6.2%
m 5238
 
5.6%
t 4719
 
5.0%
b 4656
 
5.0%
i 4251
 
4.5%
Other values (17) 28933
31.0%

model
Categorical

Distinct524
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
amg
 
535
corolla
 
442
vezel
 
406
cerato
 
348
c180
 
339
Other values (519)
12930 

Length

Max length13
Median length11
Mean length4.6742667
Min length1

Characters and Unicode

Total characters70114
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)0.7%

Sample

1st rowcorolla
2nd rowestima
3rd rowc3
4th rowgrand
5th rowe200

Common Values

ValueCountFrequency (%)
amg 535
 
3.6%
corolla 442
 
2.9%
vezel 406
 
2.7%
cerato 348
 
2.3%
c180 339
 
2.3%
2 334
 
2.2%
civic 329
 
2.2%
fit 269
 
1.8%
3 263
 
1.8%
jazz 255
 
1.7%
Other values (514) 11480
76.5%

Length

2023-09-03T00:09:50.945184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
amg 535
 
3.6%
corolla 442
 
2.9%
vezel 406
 
2.7%
cerato 348
 
2.3%
c180 339
 
2.3%
2 334
 
2.2%
civic 329
 
2.2%
fit 269
 
1.8%
3 263
 
1.8%
jazz 255
 
1.7%
Other values (514) 11480
76.5%

Most occurring characters

ValueCountFrequency (%)
a 7336
 
10.5%
e 5792
 
8.3%
r 4867
 
6.9%
i 4678
 
6.7%
c 4218
 
6.0%
o 3902
 
5.6%
l 3843
 
5.5%
t 3700
 
5.3%
s 3132
 
4.5%
0 3053
 
4.4%
Other values (27) 25593
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57847
82.5%
Decimal Number 11902
 
17.0%
Dash Punctuation 365
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7336
12.7%
e 5792
10.0%
r 4867
 
8.4%
i 4678
 
8.1%
c 4218
 
7.3%
o 3902
 
6.7%
l 3843
 
6.6%
t 3700
 
6.4%
s 3132
 
5.4%
n 2458
 
4.2%
Other values (16) 13921
24.1%
Decimal Number
ValueCountFrequency (%)
0 3053
25.7%
2 1814
15.2%
1 1767
14.8%
3 1435
12.1%
8 1169
 
9.8%
5 1063
 
8.9%
6 645
 
5.4%
4 634
 
5.3%
7 185
 
1.6%
9 137
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57847
82.5%
Common 12267
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7336
12.7%
e 5792
10.0%
r 4867
 
8.4%
i 4678
 
8.1%
c 4218
 
7.3%
o 3902
 
6.7%
l 3843
 
6.6%
t 3700
 
6.4%
s 3132
 
5.4%
n 2458
 
4.2%
Other values (16) 13921
24.1%
Common
ValueCountFrequency (%)
0 3053
24.9%
2 1814
14.8%
1 1767
14.4%
3 1435
11.7%
8 1169
 
9.5%
5 1063
 
8.7%
6 645
 
5.3%
4 634
 
5.2%
- 365
 
3.0%
7 185
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7336
 
10.5%
e 5792
 
8.3%
r 4867
 
6.9%
i 4678
 
6.7%
c 4218
 
6.0%
o 3902
 
5.6%
l 3843
 
5.5%
t 3700
 
5.3%
s 3132
 
4.5%
0 3053
 
4.4%
Other values (27) 25593
36.5%

manufactured
Real number (ℝ)

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.7703
Minimum1933
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:51.262922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1933
5-th percentile2008
Q12012
median2016
Q32018
95-th percentile2020
Maximum2021
Range88
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9879627
Coefficient of variation (CV)0.0019793634
Kurtosis18.379162
Mean2014.7703
Median Absolute Deviation (MAD)2
Skewness-1.7934879
Sum30221555
Variance15.903847
MonotonicityNot monotonic
2023-09-03T00:09:51.547849image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2016 2263
15.1%
2018 1901
12.7%
2017 1745
11.6%
2019 1598
10.7%
2015 1427
9.5%
2011 967
6.4%
2008 945
6.3%
2009 702
 
4.7%
2014 678
 
4.5%
2020 670
 
4.5%
Other values (17) 2104
14.0%
ValueCountFrequency (%)
1933 1
 
< 0.1%
1962 1
 
< 0.1%
1964 1
 
< 0.1%
1969 1
 
< 0.1%
1972 1
 
< 0.1%
1976 1
 
< 0.1%
1978 2
 
< 0.1%
1982 1
 
< 0.1%
2003 3
< 0.1%
2004 7
< 0.1%
ValueCountFrequency (%)
2021 113
 
0.8%
2020 670
 
4.5%
2019 1598
10.7%
2018 1901
12.7%
2017 1745
11.6%
2016 2263
15.1%
2015 1427
9.5%
2014 678
 
4.5%
2013 394
 
2.6%
2012 473
 
3.2%

type_of_vehicle
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
suv
3385 
luxury sedan
3119 
mid-sized sedan
2554 
hatchback
1934 
sports car
1919 
Other values (5)
2089 

Length

Max length15
Median length12
Mean length8.7926667
Min length3

Characters and Unicode

Total characters131890
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowmid-sized sedan
2nd rowmpv
3rd rowsuv
4th rowmpv
5th rowluxury sedan

Common Values

ValueCountFrequency (%)
suv 3385
22.6%
luxury sedan 3119
20.8%
mid-sized sedan 2554
17.0%
hatchback 1934
12.9%
sports car 1919
12.8%
mpv 1737
11.6%
stationwagon 345
 
2.3%
unknown 5
 
< 0.1%
van 1
 
< 0.1%
bus/mini bus 1
 
< 0.1%

Length

2023-09-03T00:09:51.864740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-03T00:09:52.334076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 5673
25.1%
suv 3385
15.0%
luxury 3119
13.8%
mid-sized 2554
11.3%
hatchback 1934
 
8.6%
sports 1919
 
8.5%
car 1919
 
8.5%
mpv 1737
 
7.7%
stationwagon 345
 
1.5%
unknown 5
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 15797
 
12.0%
a 12151
 
9.2%
d 10781
 
8.2%
u 9630
 
7.3%
e 8227
 
6.2%
7593
 
5.8%
r 6957
 
5.3%
n 6380
 
4.8%
c 5787
 
4.4%
i 5455
 
4.1%
Other values (16) 43132
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121742
92.3%
Space Separator 7593
 
5.8%
Dash Punctuation 2554
 
1.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 15797
13.0%
a 12151
 
10.0%
d 10781
 
8.9%
u 9630
 
7.9%
e 8227
 
6.8%
r 6957
 
5.7%
n 6380
 
5.2%
c 5787
 
4.8%
i 5455
 
4.5%
v 5123
 
4.2%
Other values (13) 35454
29.1%
Space Separator
ValueCountFrequency (%)
7593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2554
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121742
92.3%
Common 10148
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 15797
13.0%
a 12151
 
10.0%
d 10781
 
8.9%
u 9630
 
7.9%
e 8227
 
6.8%
r 6957
 
5.7%
n 6380
 
5.2%
c 5787
 
4.8%
i 5455
 
4.5%
v 5123
 
4.2%
Other values (13) 35454
29.1%
Common
ValueCountFrequency (%)
7593
74.8%
- 2554
 
25.2%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 15797
 
12.0%
a 12151
 
9.2%
d 10781
 
8.2%
u 9630
 
7.3%
e 8227
 
6.2%
7593
 
5.8%
r 6957
 
5.3%
n 6380
 
4.8%
c 5787
 
4.4%
i 5455
 
4.1%
Other values (16) 43132
32.7%

power
Real number (ℝ)

Distinct257
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.80447
Minimum38
Maximum735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:52.855188image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile73
Q190
median110
Q3150
95-th percentile309
Maximum735
Range697
Interquartile range (IQR)60

Descriptive statistics

Standard deviation75.927341
Coefficient of variation (CV)0.5590931
Kurtosis6.1114279
Mean135.80447
Median Absolute Deviation (MAD)25
Skewness2.3406579
Sum2037067
Variance5764.9611
MonotonicityNot monotonic
2023-09-03T00:09:53.297042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 904
 
6.0%
135 681
 
4.5%
96 604
 
4.0%
115 582
 
3.9%
85 555
 
3.7%
80 501
 
3.3%
88 446
 
3.0%
100 431
 
2.9%
73 427
 
2.8%
92 407
 
2.7%
Other values (247) 9462
63.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
40 2
 
< 0.1%
45 5
 
< 0.1%
47 21
0.1%
48 3
 
< 0.1%
50 2
 
< 0.1%
51 2
 
< 0.1%
54 3
 
< 0.1%
55 1
 
< 0.1%
57 26
0.2%
ValueCountFrequency (%)
735 1
 
< 0.1%
552 3
 
< 0.1%
545 2
 
< 0.1%
541 3
 
< 0.1%
533 2
 
< 0.1%
530 9
0.1%
515 13
0.1%
507 1
 
< 0.1%
493 9
0.1%
489 1
 
< 0.1%

engine_cap
Real number (ℝ)

Distinct228
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1972.5651
Minimum647
Maximum6752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:53.733347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile1199
Q11496
median1598
Q31998
95-th percentile3696
Maximum6752
Range6105
Interquartile range (IQR)502

Descriptive statistics

Standard deviation825.76358
Coefficient of variation (CV)0.41862424
Kurtosis8.3143575
Mean1972.5651
Median Absolute Deviation (MAD)266
Skewness2.5631736
Sum29588477
Variance681885.5
MonotonicityNot monotonic
2023-09-03T00:09:54.162143image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1496 1601
 
10.7%
1998 1106
 
7.4%
1591 802
 
5.3%
1598 760
 
5.1%
1595 690
 
4.6%
1991 507
 
3.4%
1499 466
 
3.1%
1997 446
 
3.0%
1984 443
 
3.0%
1498 322
 
2.1%
Other values (218) 7857
52.4%
ValueCountFrequency (%)
647 1
 
< 0.1%
658 2
 
< 0.1%
659 5
 
< 0.1%
796 1
 
< 0.1%
988 5
 
< 0.1%
989 6
 
< 0.1%
996 9
 
0.1%
998 49
 
0.3%
999 186
1.2%
1086 17
 
0.1%
ValueCountFrequency (%)
6752 7
 
< 0.1%
6749 12
 
0.1%
6592 30
0.2%
6498 18
0.1%
6262 5
 
< 0.1%
6208 9
 
0.1%
5999 3
 
< 0.1%
5998 32
0.2%
5980 2
 
< 0.1%
5950 12
 
0.1%

curb_weight
Real number (ℝ)

Distinct651
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1471.6709
Minimum2
Maximum2905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:54.630064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1068
Q11280
median1430
Q31625
95-th percentile1990
Maximum2905
Range2903
Interquartile range (IQR)345

Descriptive statistics

Standard deviation283.9995
Coefficient of variation (CV)0.19297758
Kurtosis1.2615645
Mean1471.6709
Median Absolute Deviation (MAD)180
Skewness0.84401511
Sum22075064
Variance80655.714
MonotonicityNot monotonic
2023-09-03T00:09:55.050647image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1190 380
 
2.5%
1430 378
 
2.5%
1425 250
 
1.7%
1615 235
 
1.6%
1345 229
 
1.5%
1480 225
 
1.5%
1610 202
 
1.3%
1310 186
 
1.2%
1195 186
 
1.2%
1285 184
 
1.2%
Other values (641) 12545
83.6%
ValueCountFrequency (%)
2 1
 
< 0.1%
780 1
 
< 0.1%
786 1
 
< 0.1%
795 1
 
< 0.1%
800 3
< 0.1%
805 1
 
< 0.1%
806 1
 
< 0.1%
830 1
 
< 0.1%
840 4
< 0.1%
852 4
< 0.1%
ValueCountFrequency (%)
2905 1
 
< 0.1%
2815 5
< 0.1%
2760 2
 
< 0.1%
2745 2
 
< 0.1%
2730 1
 
< 0.1%
2685 3
< 0.1%
2670 1
 
< 0.1%
2635 3
< 0.1%
2625 1
 
< 0.1%
2600 2
 
< 0.1%

no_of_owners
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.92
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:55.446196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2515475
Coefficient of variation (CV)0.65184765
Kurtosis1.7059348
Mean1.92
Median Absolute Deviation (MAD)0
Skewness1.50144
Sum28800
Variance1.5663711
MonotonicityNot monotonic
2023-09-03T00:09:55.755317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 7770
51.8%
2 3675
24.5%
3 1769
 
11.8%
4 938
 
6.3%
5 467
 
3.1%
6 381
 
2.5%
ValueCountFrequency (%)
1 7770
51.8%
2 3675
24.5%
3 1769
 
11.8%
4 938
 
6.3%
5 467
 
3.1%
6 381
 
2.5%
ValueCountFrequency (%)
6 381
 
2.5%
5 467
 
3.1%
4 938
 
6.3%
3 1769
 
11.8%
2 3675
24.5%
1 7770
51.8%

mileage
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4936
Distinct (%)34.6%
Missing741
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean73664.611
Minimum1
Maximum386000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:56.410564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7000
Q135072
median67444
Q3105000
95-th percentile160000
Maximum386000
Range385999
Interquartile range (IQR)69928

Descriptive statistics

Standard deviation48386.077
Coefficient of variation (CV)0.65684291
Kurtosis0.45917871
Mean73664.611
Median Absolute Deviation (MAD)33944
Skewness0.70874696
Sum1.0503837 × 109
Variance2.3412125 × 109
MonotonicityNot monotonic
2023-09-03T00:09:56.867956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80000 148
 
1.0%
50000 119
 
0.8%
120000 118
 
0.8%
90000 110
 
0.7%
60000 109
 
0.7%
130000 104
 
0.7%
110000 103
 
0.7%
70000 102
 
0.7%
65000 93
 
0.6%
140000 93
 
0.6%
Other values (4926) 13160
87.7%
(Missing) 741
 
4.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 12
0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
18 5
 
< 0.1%
20 19
0.1%
ValueCountFrequency (%)
386000 1
< 0.1%
340000 1
< 0.1%
334000 1
< 0.1%
330000 1
< 0.1%
318000 1
< 0.1%
311000 1
< 0.1%
300000 2
< 0.1%
298223 1
< 0.1%
288000 1
< 0.1%
285000 1
< 0.1%

price
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2630
Distinct (%)18.1%
Missing445
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean108181.28
Minimum1900
Maximum2388777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2023-09-03T00:09:57.277907image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile21500
Q157800
median78000
Q3119800
95-th percentile271988
Maximum2388777
Range2386877
Interquartile range (IQR)62000

Descriptive statistics

Standard deviation112136.56
Coefficient of variation (CV)1.0365616
Kurtosis47.289295
Mean108181.28
Median Absolute Deviation (MAD)26200
Skewness5.2986911
Sum1.5745785 × 109
Variance1.2574607 × 1010
MonotonicityNot monotonic
2023-09-03T00:09:57.661363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69800 169
 
1.1%
65800 150
 
1.0%
58800 130
 
0.9%
66800 129
 
0.9%
72800 123
 
0.8%
68800 121
 
0.8%
75800 121
 
0.8%
59800 120
 
0.8%
76800 119
 
0.8%
79800 119
 
0.8%
Other values (2620) 13254
88.4%
(Missing) 445
 
3.0%
ValueCountFrequency (%)
1900 1
 
< 0.1%
3500 1
 
< 0.1%
3999 1
 
< 0.1%
4800 2
< 0.1%
5000 1
 
< 0.1%
5300 1
 
< 0.1%
5500 3
< 0.1%
5800 2
< 0.1%
6000 3
< 0.1%
6300 1
 
< 0.1%
ValueCountFrequency (%)
2388777 1
< 0.1%
2000000 1
< 0.1%
1700000 1
< 0.1%
1535555 1
< 0.1%
1480000 1
< 0.1%
1390900 1
< 0.1%
1380500 1
< 0.1%
1330000 1
< 0.1%
1300000 1
< 0.1%
1250000 2
< 0.1%

Interactions

2023-09-03T00:09:44.870091image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:25.138007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:28.415857image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:30.750005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:33.898295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:36.963568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:39.747172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:42.321095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:45.190875image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:25.453383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:28.672621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:31.046401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:34.399315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:37.251987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:40.051772image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:42.639323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:45.464639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:25.724600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:28.974255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:31.325500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:34.675536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:37.701213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:40.346204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:42.931891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:45.763978image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:26.021611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:29.255534image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:31.610404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:34.998107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:38.123317image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:40.692314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:43.245003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:46.348387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:26.295506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:29.570701image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:31.926161image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:35.303237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:38.462542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:40.993543image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:43.550842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:46.659848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:26.715886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:29.857762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:32.547542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:35.955685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:38.748077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:41.288313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:43.891258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:46.984950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:27.291079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:30.137976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:32.862863image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:36.271014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:39.075672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:41.568285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:44.195364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:47.371215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:27.938565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:30.457452image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:33.395059image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:36.645887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:39.424769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:41.936991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-09-03T00:09:44.537473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-09-03T00:09:57.979739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
listing_idmanufacturedpowerengine_capcurb_weightno_of_ownersmileagepricemaketype_of_vehicle
listing_id1.000-0.029-0.068-0.063-0.0570.0080.048-0.0790.1480.025
manufactured-0.0291.000-0.049-0.1910.029-0.685-0.8260.4840.4220.376
power-0.068-0.0491.0000.8140.7990.115-0.0640.6670.4140.284
engine_cap-0.063-0.1910.8141.0000.7790.1860.0790.4910.5260.252
curb_weight-0.0570.0290.7990.7791.0000.010-0.0740.6310.4370.266
no_of_owners0.008-0.6850.1150.1860.0101.0000.573-0.2370.1510.172
mileage0.048-0.826-0.0640.079-0.0740.5731.000-0.5490.0920.132
price-0.0790.4840.6670.4910.631-0.237-0.5491.0000.4560.119
make0.1480.4220.4140.5260.4370.1510.0920.4561.0000.528
type_of_vehicle0.0250.3760.2840.2520.2660.1720.1320.1190.5281.000

Missing values

2023-09-03T00:09:47.846469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-03T00:09:48.570029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-03T00:09:49.043901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

listing_idurlmakemodelmanufacturedtype_of_vehiclepowerengine_capcurb_weightno_of_ownersmileageprice
01023911https://www.sgcarmart.com/listing/1023911toyotacorolla2009mid-sized sedan8114961130.03127450.020800.0
11022346https://www.sgcarmart.com/listing/1022346toyotaestima2007mpv12523621710.04103000.052500.0
21017880https://www.sgcarmart.com/listing/1017880citroenc32018suv8111991203.0135000.068000.0
31022468https://www.sgcarmart.com/listing/1022468renaultgrand2015mpv8114611539.0180848.052800.0
41026440https://www.sgcarmart.com/listing/1026440mercedes-benze2002009luxury sedan13517961615.02116199.073800.0
51019571https://www.sgcarmart.com/listing/1019571kiacerato2018mid-sized sedan9315911287.0222000.056800.0
61023663https://www.sgcarmart.com/listing/1023663toyotacamry2007luxury sedan10819981530.04238400.010700.0
7973358https://www.sgcarmart.com/listing/973358bmw318i2017luxury sedan10014991425.0151400.0112900.0
81024507https://www.sgcarmart.com/listing/1024507hondavezel2016suv9614961190.0272000.057800.0
91016664https://www.sgcarmart.com/listing/1016664hondacivic2019mid-sized sedan9215971237.0127000.075800.0
listing_idurlmakemodelmanufacturedtype_of_vehiclepowerengine_capcurb_weightno_of_ownersmileageprice
14990985437https://www.sgcarmart.com/listing/985437volkswagenbeetle2010hatchback7515951240.0363886.088888.0
149911004248https://www.sgcarmart.com/listing/1004248hondae2016mpv12923561769.0157600.084800.0
14992974393https://www.sgcarmart.com/listing/974393toyotaestima2010mpv12523621780.02151800.078800.0
149931016298NaNhyundaiavante2009mid-sized sedan8915911264.04108665.019800.0
149941018560https://www.sgcarmart.com/listing/1018560toyotac-hr2017suv9017971440.0158000.071800.0
149951028368https://www.sgcarmart.com/listing/1028368toyotaprius2018mpv10017981500.0153716.093800.0
149961030303https://www.sgcarmart.com/listing/1030303bmw530i2017luxury sedan18519981540.0256000.0155000.0
149971021704https://www.sgcarmart.com/listing/1021704nissanteana2008luxury sedan13424961565.01103794.054800.0
149981025480https://www.sgcarmart.com/listing/1025480toyotacamry2011luxury sedan12323621540.03149000.077800.0
149991017540https://www.sgcarmart.com/listing/1017540mercedes-benzc1802012luxury sedan11515971500.0294000.081500.0

Duplicate rows

Most frequently occurring

listing_idurlmakemodelmanufacturedtype_of_vehiclepowerengine_capcurb_weightno_of_ownersmileageprice# duplicates
0894308https://www.sgcarmart.com/listing/894308kiasportage2017suv11419991500.0153708.077800.02
1981956https://www.sgcarmart.com/listing/981956lexusrx2011suv13826721840.03118200.089800.02
2986149https://www.sgcarmart.com/listing/986149lexusis2019luxury sedan18019981620.0210000.0149800.02
3991713https://www.sgcarmart.com/listing/991713lexuses2016luxury sedan13524941615.0134900.0103800.02
4991776https://www.sgcarmart.com/listing/991776hyundaiavante2019mid-sized sedan9315911345.0130000.071800.02
5992386https://www.sgcarmart.com/listing/992386kiacerato2017mid-sized sedan9515911295.0251240.058800.02
6995501https://www.sgcarmart.com/listing/995501lexusrx2019suv17519981890.0131000.0189800.02
7995581https://www.sgcarmart.com/listing/995581bmw318i2018luxury sedan10014991425.0170000.0119800.02
8996726https://www.sgcarmart.com/listing/996726toyotac-hr2018suv9017971440.0156214.079800.02
91000777https://www.sgcarmart.com/listing/1000777audia82018luxury sedan25029952020.0115200.0304999.02